Architect - Platform Engineering - USA
USD 150K-216K (estimate) Senior-level Full Time
Tasks
- Architect MLOps strategy
- Build GKE first container ML platform
- Collaborate with cross-functional teams
- Conduct architecture reviews and troubleshooting
- Design ML/LLM pipelines
- Enable lineage tracking and auditability
- Ensure security, governance, compliance
- Evaluate orchestration tools
- Implement CI/CD automation
- Implement LLMOps workflows
- Implement model monitoring and drift detection
- Manage prompt version governance
- Mentor engineers on MLOps best practices
- Own model training validation deployment versioning
Perks/Benefits
- N/A
Skills/Tech-stack
AI Model Monitoring | AI Pipelines | API Gateway | Airflow | Apache Spark | Artifact governance | BentoML | BigQuery | BigQuery ML | CI/CD | Cloud Build | Cloud Deploy | Cloud Functions | Cloud IAM | Cloud Monitoring | Cloud platform | Containerization | Databricks | Drift Detection | Feature Engineering | Feature Store | GKE | Google Cloud | Google Cloud Platform | Google Kubernetes | Google Kubernetes Engine | Grafana | KServe | Kubeflow | Kubeflow Pipelines | Kubernetes Engine | LLMOps | LMM Operations | Lineage Tracking | ML operations | MLflow | Model Deployment | Model Governance | Model Monitoring | Model Registry | Model Training | Model Validation | Prometheus | Prompt versioning | Python | SQL | Secrets management | Seldon | Snowflake | Vertex AI | Vertex AI Feature Store | Vertex AI Model Monitoring | Vertex AI Model Registry | Vertex AI pipelines
Education
N/A
Roles
AI | AI Platform Architect | Architect | MLOps Architect | Platform | Platform Architect
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